The following description relates to reorganizing data in machine-readable mediums, data processing systems, and in memory to reduce the redundancy of the data and allow the available system resources to be used efficiently.
Companies handle and store large amounts of data. Such data can consume significant information technology resources, both during handling and processing, when it occupies space in memory (e.g., random access memory, RAM) and during long-term storage, when it occupies space on a machine-readable medium (e.g., hard disk or magnetic tape). For example, businesses may not only need to spend a portion of their resources on computers for workers, but for the data that the workers collect, process, and generate. Resources may also have to be spent for servers, databases, and systems to store the data, as well as warehouses and facilities to store those components. Businesses may have to allocate resources and personnel for the management of the data, and the ability to turn their data into useful organizational knowledge.
Certain techniques of organizing, formatting, or storing business data, when employed in certain business scenarios, can contribute to various amounts of data redundancy. For example, companies often store large volumes of business data in data models, such as cube-like data models, that are optimized for a large number of business scenarios. Even though the cube-like data models may offer advantages in a particular business scenario, the data models may be superfluous or irrelevant for other business scenarios. When businesses have data models for a significant amount of irrelevant scenarios, the stored data using such models may include redundancies and may occupy more space than necessary in memory. If the data can be reorganized in such a way that the amount of redundancy is reduced, then the business can reduce the costs of storing and managing the data.